






'''simple docstring'''







from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__lowerCAmelCase        =							transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def       __UpperCamelCase					(      lowercase_       :  Any   ):
       """simple docstring"""




       if isinstance(lowercase_   ,       torch.Tensor   ):
              return image
       elif isinstance(lowercase_   ,       PIL.Image.Image   ):
              a_						     =			[image]

       a_						     =			[trans(img.convert('RGB'   )   ) for img in image]
       a_						     =			torch.stack(lowercase_   )
       return image




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         super().__init__()

         # make sure scheduler can always be converted to DDIM
         a_						     =			DDIMScheduler.from_config(scheduler.config	)

         self.register_modules(unet=UpperCamelCase__   ,	scheduler=UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         if strength < 0 or strength > 1:
                raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}'	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			min(int(num_inference_steps * strength	)   ,	UpperCamelCase__	)

         a_						     =			max(num_inference_steps - init_timestep   ,	0	)
         a_						     =			self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__=None	):






         """simple docstring"""


         if not isinstance(UpperCamelCase__   ,	(torch.Tensor, PIL.Image.Image, list)	):
                raise ValueError(
                    f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCamelCase__	)}'	)

         a_						     =			image.to(device=UpperCamelCase__   ,	dtype=UpperCamelCase__	)

         if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) and len(UpperCamelCase__	) != batch_size:
                raise ValueError(
                    f'You have passed a list of generators of length {len(UpperCamelCase__	)}, but requested an effective batch'
                    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'	)

         a_						     =			init_latents.shape
         a_						     =			randn_tensor(UpperCamelCase__   ,	generator=UpperCamelCase__   ,	device=UpperCamelCase__   ,	dtype=UpperCamelCase__	)

         # get latents
         print('add noise to latents at timestep'   ,	UpperCamelCase__	)
         a_						     =			self.scheduler.add_noise(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	)
         a_						     =			init_latents

         return latents







  @torch.no_grad()
  def __call__(			self   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = 0.8   ,	UpperCamelCase__ = 1   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = 0.0   ,	UpperCamelCase__ = 50   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = "pil"   ,	UpperCamelCase__ = True   ,	):






         """simple docstring"""


         self.check_inputs(UpperCamelCase__	)

         # 2. Preprocess image
         a_						     =			preprocess(UpperCamelCase__	)

         # 3. set timesteps
         self.scheduler.set_timesteps(UpperCamelCase__   ,	device=self.device	)
         a_     ,	a_						     =			self.get_timesteps(UpperCamelCase__   ,	UpperCamelCase__   ,	self.device	)
         a_						     =			timesteps[:1].repeat(UpperCamelCase__	)

         # 4. Prepare latent variables
         a_						     =			self.prepare_latents(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	self.unet.dtype   ,	self.device   ,	UpperCamelCase__	)
         a_						     =			latents

         # 5. Denoising loop
         for t in self.progress_bar(UpperCamelCase__	):
                # 1. predict noise model_output
                a_						     =			self.unet(UpperCamelCase__   ,	UpperCamelCase__	).sample

                # 2. predict previous mean of image x_t-1 and add variance depending on eta
                # eta corresponds to η in paper and should be between [0, 1]
                # do x_t -> x_t-1
                a_						     =			self.scheduler.step(
                    UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	eta=UpperCamelCase__   ,	use_clipped_model_output=UpperCamelCase__   ,	generator=UpperCamelCase__   ,	).prev_sample

         a_						     =			(image / 2 + 0.5).clamp(0   ,	1	)
         a_						     =			image.cpu().permute(0   ,	2   ,	3   ,	1	).numpy()
         if output_type == "pil":
                a_						     =			self.numpy_to_pil(UpperCamelCase__	)

         if not return_dict:
                return (image, latent_timestep.item())

         return ImagePipelineOutput(images=UpperCamelCase__	)












'''simple docstring'''







import math

import qiskit


def       __UpperCamelCase					(      lowercase_       :  int = 1   ,       lowercase_       :  int = 1   ,       lowercase_       :  int = 1   ):
       """simple docstring"""




       if (
           isinstance(lowercase_   ,       lowercase_   )
           or isinstance(lowercase_   ,       lowercase_   )
           or isinstance(lowercase_   ,       lowercase_   )
       ):
              raise TypeError('inputs must be integers.'   )

       if (input_a < 0) or (input_a < 0) or (carry_in < 0):
              raise ValueError('inputs must be positive.'   )

       if (
           (math.floor(lowercase_   ) != input_a)
           or (math.floor(lowercase_   ) != input_a)
           or (math.floor(lowercase_   ) != carry_in)
       ):
              raise ValueError('inputs must be exact integers.'   )

       if (input_a > 2) or (input_a > 2) or (carry_in > 2):
              raise ValueError('inputs must be less or equal to 2.'   )

       # build registers
       a_						     =			qiskit.QuantumRegister(4   ,       'qr'   )
       a_						     =			qiskit.ClassicalRegister(2   ,       'cr'   )
       # list the entries
       a_						     =			[input_a, input_a, carry_in]

       a_						     =			qiskit.QuantumCircuit(lowercase_   ,       lowercase_   )

       for i in range(0   ,       3   ):
              if entry[i] == 2:
                     quantum_circuit.h(lowercase_   )  # for hadamard entries
              elif entry[i] == 1:
                     quantum_circuit.x(lowercase_   )  # for 1 entries
              elif entry[i] == 0:
                     quantum_circuit.i(lowercase_   )  # for 0 entries

    # build the circuit
       quantum_circuit.ccx(0   ,       1   ,       3   )  # ccx = toffoli gate
       quantum_circuit.cx(0   ,       1   )
       quantum_circuit.ccx(1   ,       2   ,       3   )
       quantum_circuit.cx(1   ,       2   )
       quantum_circuit.cx(0   ,       1   )

       quantum_circuit.measure([2, 3]   ,       lowercase_   )  # measure the last two qbits

       a_						     =			qiskit.Aer.get_backend('aer_simulator'   )
       a_						     =			qiskit.execute(lowercase_   ,       lowercase_   ,       shots=1_000   )

       return job.result().get_counts(lowercase_   )


if __name__ == "__main__":
  print(f"""Total sum count for state is: {quantum_full_adder(1, 1, 1)}""")




